code: def arrays_OI_O_BI(
      min_batch=1,
      max_batch=16,
      min_out=1,
      max_out=16,
      min_in=1,
      max_in=16,
  ):
      shapes = tuples(
          lengths(lo=min_batch, hi=max_batch),
          lengths(lo=min_in, hi=max_out),
          lengths(lo=min_in, hi=max_in),
      )
  
      def W_b_inputs(shape):
          batch_size, nr_out, nr_in = shape
          W = ndarrays_of_shape((nr_out, nr_in))
          b = ndarrays_of_shape((nr_out,))
          input_ = ndarrays_of_shape((batch_size, nr_in))
          return tuples(W, b, input_)
  
      return shapes.flatmap(W_b_inputs)
  
file_path: thinc/thinc/tests/strategies.py
imports: import numpy
  from hypothesis.extra.numpy import arrays
  from hypothesis.strategies import floats, integers, just, tuples
  from thinc.api import Linear, NumpyOps
label_desc: Use image node_id 13 for calling a global function with example usage: arrays_OI_O_BI(min_batch, max_batch, min_out, max_out, min_in, max_in) and returns: shapes, tuples